Contextual similarity.
The underlying assumption of the contextual similarity is that the more two samples semantically similar, the larger their contexts overlap.
One may consider nearest neighbors of a data point on the teacher embedding space as its context, but they often include irrelevant data when the embedding space is not sufficiently trained yet.
We thus instead consider \(k\) -reciprocal nearest neighbors as context since they are known to discover highly correlated candidates [1]}, [2]}.
The set of \(k\) -reciprocal nearest neighbors of a data point \(x_i\)  is given by
\(R_k(i) = \lbrace j|(j \in N_k(i))\wedge (i \in N_k(j))\rbrace ,\) 
